Last updated: 2024-01-17
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Knit directory: files/
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| html | 59bee55 | Andreas Chiocchetti | 2024-01-14 | Build site. |
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| Rmd | bca1a73 | Andreas Chiocchetti | 2024-01-09 | intermediate stage bulk seq |
Centering and scaling data matrix
PC_ 1
Positive: STMN2, NSG2, NEUROD6, INA, NEUROD2, BHLHE22, CXADR, SLA, GAP43, NELL2
TTC9B, GRIA2, MYT1L, LRRC7, MLLT3, LBH, UCHL1, DLX6-AS1, STMN4, OCIAD2
CNR1, SYT4, ENC1, MEF2C, SNAP25, RUNX1T1, ERBB4, SNCB, ZBTB18, GRIA1
Negative: SLC1A3, VIM, HMGB2, ZFP36L1, NUSAP1, TOP2A, DBI, PTN, B2M, MKI67
PTTG1, CLU, CD99, PTPRZ1, CDK1, SPARC, PON2, HOPX, UBE2C, METRN
TTYH1, TPX2, PBK, CENPF, ANXA5, MT2A, BCAN, SOX9, PEA15, HSPB1
PC_ 2
Positive: MKI67, UBE2C, TOP2A, NUSAP1, TPX2, CENPF, KIF2C, DLGAP5, ASPM, BIRC5
KNL1, PIMREG, CDC20, CDCA8, NUF2, PBK, CDK1, SGO1, PTTG1, HMGB2
KIF11, PLK1, CCNA2, CKAP2L, KIFC1, GTSE1, CCNB1, NDC80, CENPE, MAD2L1
Negative: CLU, ATP1B2, PTN, AQP4, HOPX, PON2, TFPI, ANOS1, ATP1A2, TTYH1
SPARC, BCAN, SLC1A3, APOE, PSAT1, VIM, PEA15, FAM107A, PTPRZ1, HES1
TIMP3, IL33, LRRC3B, CSPG5, S1PR1, SLCO1C1, SCD, VCAM1, TNC, IQGAP2
PC_ 3
Positive: NEUROD6, NELL2, NEUROD2, MEF2C, BHLHE22, GAP43, ARPP21, SATB2, SERPINI1, SYT4
ZBTB18, SLA, FAM49A, GPR22, NEFM, CAMK2B, GPR85, SNCB, CSRP2, NSG2
CXADR, LINGO1, SATB2-AS1, PLXNA4, DAB1, OCIAD2, GPRIN3, NRN1, PCLO, FAM162A
Negative: DLX6-AS1, PLS3, SCGN, DLX2, DLX5, DLX1, SOX2-OT, GAD2, CALB2, SP9
PDZRN3, ERBB4, RND3, ID4, SMOC1, C1orf61, NNAT, GAD1, WLS, SOX9
NRIP3, TOX3, ST18, HMGN2, AMBN, NRXN3, CDCA7, DBI, BCAN, PCDH9
PC_ 4
Positive: ADM, VEGFA, DDIT4, BNIP3, IGFBP2, P4HA1, PLOD2, EGLN3, SLC16A3, SLC2A1
FAM162A, ENO1, STC2, AKAP12, PGK1, IGFBP5, GAPDH, PDK1, SLC16A1, TPI1
AK4, CEBPB, PKM, MIR210HG, HERPUD1, SHMT2, EMX2, HK2, BHLHE40, CDKN1A
Negative: NTRK2, AQP4, SPARCL1, APOE, GJA1, CST3, SPON1, MEF2C, PMP2, ANOS1
TFPI, S100B, BCAN, CHL1, STMN2, DCLK1, CSPG5, NKAIN4, BBOX1, VCAM1
LINC01896, AGT, CALM1, SATB2, ANGPT1, RANBP3L, SERPINI1, WLS, NELL2, GFAP
PC_ 5
Positive: ENC1, TMEM158, BHLHE22, NEUROD2, EZR, SLA, CSRP2, MLLT3, CNR1, PHLDA1
NEUROD6, CNTNAP2, LHX2, ADRA2A, NKAIN3, ZBTB18, HES6, EOMES, EPHA3, CLMP
NHLH1, FABP7, RASGRP1, NEUROG2, SFRP1, CHRDL1, HS3ST1, PENK, GAP43, GNG5
Negative: DLX6-AS1, PLS3, SCGN, DLX2, SOX2-OT, DLX1, DLX5, PLOD2, STC2, PDZRN3
GPRIN3, GPR22, CALB2, ADM, DDIT4, CALY, BNIP3, SERPINI1, GAD2, ERBB4
VEGFA, H1F0, FAM162A, SP9, PCDH9, IGFBP5, CNTN1, CELF4, PDK1, P4HA1
Computing nearest neighbor graph
Computing SNN
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 9107
Number of edges: 410519
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9383
Number of communities: 7
Elapsed time: 1 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 9107
Number of edges: 410519
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9007
Number of communities: 9
Elapsed time: 1 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 9107
Number of edges: 410519
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8746
Number of communities: 14
Elapsed time: 1 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 9107
Number of edges: 410519
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8516
Number of communities: 14
Elapsed time: 1 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 9107
Number of edges: 410519
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8312
Number of communities: 16
Elapsed time: 1 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 9107
Number of edges: 410519
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8164
Number of communities: 19
Elapsed time: 1 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 9107
Number of edges: 410519
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8028
Number of communities: 20
Elapsed time: 1 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 9107
Number of edges: 410519
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.7900
Number of communities: 21
Elapsed time: 1 seconds
UMAP Clustering after batch correction at different resolutions
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Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 9107
Number of edges: 410519
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8746
Number of communities: 14
Elapsed time: 1 seconds
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
<simpleWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session>
Warning: Removed 1 rows containing missing values (`geom_text()`).
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Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session
18:59:32 UMAP embedding parameters a = 0.1496 b = 0.8684
Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
Also defined by 'spam'
18:59:32 Read 9107 rows and found 40 numeric columns
18:59:32 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
Also defined by 'spam'
18:59:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
18:59:34 Writing NN index file to temp file /tmp/RtmpdGKrw2/file14fe5e18f6fa
18:59:34 Searching Annoy index using 1 thread, search_k = 3000
18:59:37 Annoy recall = 100%
18:59:38 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
18:59:40 Initializing from normalized Laplacian + noise (using RSpectra)
18:59:40 Commencing optimization for 500 epochs, with 415428 positive edges
18:59:54 Optimization finished

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Warning: The following features are not present in the object: FEN1, MLF1IP,
RAD51, not searching for symbol synonyms
Warning: The following features are not present in the object: FAM64A, HN1, not
searching for symbol synonyms

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0mM 5mM
0mM 1.0000000 0.6908695
5mM 0.6908695 1.0000000
Using type as id variables

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Feature plots UMAP
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Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
ℹ Please use tidy evaluation idioms with `aes()`.
ℹ See also `vignette("ggplot2-in-packages")` for more information.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
generated.
Feature plots PCA
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Calculating cluster 0
For a (much!) faster implementation of the Wilcoxon Rank Sum Test,
(default method for FindMarkers) please install the presto package
--------------------------------------------
install.packages('devtools')
devtools::install_github('immunogenomics/presto')
--------------------------------------------
After installation of presto, Seurat will automatically use the more
efficient implementation (no further action necessary).
This message will be shown once per session
Calculating cluster 1
Calculating cluster 2
Calculating cluster 3
Calculating cluster 4
Calculating cluster 5
Calculating cluster 6
Calculating cluster 7
Calculating cluster 8
Calculating cluster 9
Calculating cluster 10
Calculating cluster 11
Calculating cluster 12
Calculating cluster 13
Warning in DoHeatmap(seurat_integrated, features = top10$gene, slot =
"scale.data"): The following features were omitted as they were not found in
the scale.data slot for the RNA assay: CYP4F26P, PPARG, IGLV1-51, TRHDE-AS1,
AL133375.1, DCHS2, AC026471.3, CNOT6LP1, AC103810.2, ARMC3, LINC01497,
AC084125.2, AC120036.1, POU4F1, NAMPTP1, AC010320.1, LHX5-AS1, AC091182.1,
NKX2-5, HOXD9, MYOD1, LHX5, ULBP1, AL157400.2, UPK1A-AS1, AC099850.3, FAM72C,
FBLN2, HSPB3, MYO3B, IMPG2, VIPR2, LINC00689, TESK2, AP001972.3, DENND1C,
CRYBG2, MCHR1, RELN, EPS8L2, ASCL2, CABP7, FXYD3, PI16, KCNJ16, GJB2, FIBIN,
MME, OXTR, DMRTA1, AC087632.1, KANK2, CDC45, AC099754.1, AC092112.1, IL12A,
CASP1, FMO1, TMEM244, GPR61, AL139275.1, AC010931.2, AKAIN1, CEMIP, LRTM2,
UNC5A, LAMB3, PTGFR, SVEP1, MYOT, CACNA2D3, PTPRR, RGN, THRB

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Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Using Group.1 as id variables

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url could not be opended
cannot open URL 'https://onedrive.live.com/download?id=1CE096496C465FF3!879&resid=1CE096496C465FF3!879&ithint=file%2cxlsx&authkey=!AEkKHImEdX9UvHA&wdo=2&cid=1ce096496c465ff3': HTTP status was '403 Forbidden'
trying local instead
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Using Group.1 as id variables

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Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Using Group.1 as id variables
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Using Group.1 as id variables

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The default behaviour of split.by has changed.
Separate violin plots are now plotted side-by-side.
To restore the old behaviour of a single split violin,
set split.plot = TRUE.
This message will be shown once per session.
Warning: The following requested variables were not found: BOLA2, SLX1B,
SULT1A4, SULT1A3, SPN, C16orf54, PRRT2, PAGR1, TLCD3B
Scale for x is already present.
Adding another scale for x, which will replace the existing scale.

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url could not be opended
cannot open URL 'https://onedrive.live.com/download?id=1CE096496C465FF3!878&resid=1CE096496C465FF3!878&ithint=file%2cxlsx&authkey=!AMv00cMy5Pg7LLA&wdo=2&cid=1ce096496c465ff3': HTTP status was '403 Forbidden'
trying local instead
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Using Group.1 as id variables

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Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Using Group.1 as id variables
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Error in .subscript.2ary(x, i, , drop = TRUE) : subscript out of bounds
Using Group.1 as id variables

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Warning: The following requested variables were not found: TDO2 , IDO1, IDO2,
KYNU
Scale for x is already present.
Adding another scale for x, which will replace the existing scale.

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Detected custom background input, domain scope is set to 'custom'
[1] "result" "meta"
Loading required package: kableExtra
Attaching package: 'kableExtra'
The following object is masked from 'package:dplyr':
group_rows
Loading required package: compareGroups
Loading required package: pheatmap
Loading required package: DESeq2
Attaching package: 'DESeq2'
The following object is masked from 'package:CATALYST':
plotCounts
The following object is masked from 'package:scater':
fpkm

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R version 4.3.1 (2023-06-16)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.2 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: Etc/UTC
tzcode source: system (glibc)
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] DESeq2_1.40.2 pheatmap_1.0.12
[3] compareGroups_4.7.2 kableExtra_1.3.4
[5] clustree_0.5.1 ggraph_2.1.0
[7] CATALYST_1.24.0 reshape2_1.4.4
[9] pals_1.8 gprofiler2_0.2.2
[11] viridis_0.6.4 viridisLite_0.4.2
[13] cowplot_1.1.2 randomcoloR_1.1.0.1
[15] RCurl_1.98-1.13 RColorBrewer_1.1-3
[17] data.table_1.14.10 lubridate_1.9.3
[19] forcats_1.0.0 stringr_1.5.1
[21] dplyr_1.1.4 purrr_1.0.2
[23] readr_2.1.4 tidyr_1.3.0
[25] tibble_3.2.1 tidyverse_2.0.0
[27] scater_1.28.0 scuttle_1.10.3
[29] Seurat_5.0.1 SeuratObject_5.0.1
[31] sp_2.1-2 SingleCellExperiment_1.24.0
[33] ggpubr_0.6.0 ggplot2_3.4.4
[35] SingleR_2.2.0 SummarizedExperiment_1.32.0
[37] Biobase_2.62.0 GenomicRanges_1.54.1
[39] GenomeInfoDb_1.38.1 IRanges_2.36.0
[41] S4Vectors_0.40.2 BiocGenerics_0.48.1
[43] MatrixGenerics_1.14.0 matrixStats_1.1.0
[45] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] dichromat_2.0-0.1 nnet_7.3-19
[3] goftest_1.2-3 DT_0.31
[5] TH.data_1.1-2 vctrs_0.6.5
[7] spatstat.random_3.2-2 digest_0.6.33
[9] png_0.1-8 shape_1.4.6
[11] git2r_0.33.0 ggrepel_0.9.4
[13] httpcode_0.3.0 deldir_2.0-2
[15] parallelly_1.36.0 fontLiberation_0.1.0
[17] MASS_7.3-60 httpuv_1.6.13
[19] foreach_1.5.2 withr_2.5.2
[21] ggrastr_1.0.2 xfun_0.41
[23] ellipsis_0.3.2 survival_3.5-7
[25] crul_1.4.0 ggbeeswarm_0.7.2
[27] RProtoBufLib_2.12.1 drc_3.0-1
[29] systemfonts_1.0.5 ragg_1.2.7
[31] zoo_1.8-12 GlobalOptions_0.1.2
[33] gtools_3.9.5 V8_4.4.1
[35] pbapply_1.7-2 promises_1.2.1
[37] httr_1.4.7 rstatix_0.7.2
[39] globals_0.16.2 fitdistrplus_1.1-11
[41] ps_1.7.5 rstudioapi_0.15.0
[43] pan_1.9 miniUI_0.1.1.1
[45] generics_0.1.3 processx_3.8.3
[47] curl_5.2.0 zlibbioc_1.48.0
[49] ScaledMatrix_1.8.1 polyclip_1.10-6
[51] GenomeInfoDbData_1.2.11 SparseArray_1.2.2
[53] xtable_1.8-4 doParallel_1.0.17
[55] evaluate_0.23 S4Arrays_1.2.0
[57] glmnet_4.1-8 hms_1.1.3
[59] irlba_2.3.5.1 colorspace_2.1-0
[61] ROCR_1.0-11 reticulate_1.34.0
[63] spatstat.data_3.0-3 magrittr_2.0.3
[65] lmtest_0.9-40 later_1.3.2
[67] lattice_0.22-5 mapproj_1.2.11
[69] spatstat.geom_3.2-7 future.apply_1.11.1
[71] getPass_0.2-4 scattermore_1.2
[73] XML_3.99-0.16 RcppAnnoy_0.0.21
[75] pillar_1.9.0 nlme_3.1-164
[77] iterators_1.0.14 compiler_4.3.1
[79] beachmat_2.16.0 RSpectra_0.16-1
[81] stringi_1.8.3 jomo_2.7-6
[83] minqa_1.2.6 tensor_1.5
[85] plyr_1.8.9 crayon_1.5.2
[87] abind_1.4-5 truncnorm_1.0-9
[89] chron_2.3-61 locfit_1.5-9.8
[91] graphlayouts_1.0.2 sandwich_3.1-0
[93] whisker_0.4.1 codetools_0.2-19
[95] multcomp_1.4-25 textshaping_0.3.7
[97] BiocSingular_1.16.0 openssl_2.1.1
[99] flextable_0.9.4 crosstalk_1.2.1
[101] bslib_0.6.1 GetoptLong_1.0.5
[103] plotly_4.10.3 mime_0.12
[105] splines_4.3.1 circlize_0.4.15
[107] Rcpp_1.0.11 fastDummies_1.7.3
[109] sparseMatrixStats_1.12.2 knitr_1.45
[111] utf8_1.2.4 clue_0.3-65
[113] lme4_1.1-35.1 fs_1.6.3
[115] listenv_0.9.0 checkmate_2.3.1
[117] nnls_1.5 DelayedMatrixStats_1.22.6
[119] openxlsx_4.2.5.2 ggsignif_0.6.4
[121] Matrix_1.6-4 callr_3.7.3
[123] tzdb_0.4.0 svglite_2.1.3
[125] tweenr_2.0.2 pkgconfig_2.0.3
[127] tools_4.3.1 cachem_1.0.8
[129] rvest_1.0.3 fastmap_1.1.1
[131] rmarkdown_2.25 scales_1.3.0
[133] grid_4.3.1 ica_1.0-3
[135] officer_0.6.3 broom_1.0.5
[137] sass_0.4.8 patchwork_1.2.0
[139] dotCall64_1.1-1 carData_3.0-5
[141] rpart_4.1.23 RANN_2.6.1
[143] farver_2.1.1 tidygraph_1.3.0
[145] yaml_2.3.8 cli_3.6.2
[147] writexl_1.4.2 webshot_0.5.5
[149] leiden_0.4.3.1 lifecycle_1.0.4
[151] askpass_1.2.0 uwot_0.1.16
[153] mvtnorm_1.2-4 backports_1.4.1
[155] BiocParallel_1.34.2 cytolib_2.12.1
[157] timechange_0.2.0 gtable_0.3.4
[159] rjson_0.2.21 ggridges_0.5.5
[161] progressr_0.14.0 parallel_4.3.1
[163] limma_3.56.2 jsonlite_1.8.8
[165] mitml_0.4-5 RcppHNSW_0.5.0
[167] bitops_1.0-7 openxlsx2_1.2
[169] Rtsne_0.17 FlowSOM_2.8.0
[171] spatstat.utils_3.0-4 BiocNeighbors_1.18.0
[173] zip_2.3.0 flowCore_2.12.2
[175] mice_3.16.0 jquerylib_0.1.4
[177] highr_0.10 lazyeval_0.2.2
[179] shiny_1.8.0 ConsensusClusterPlus_1.64.0
[181] htmltools_0.5.7 sctransform_0.4.1
[183] gfonts_0.2.0 glue_1.6.2
[185] spam_2.10-0 XVector_0.42.0
[187] gdtools_0.3.5 rprojroot_2.0.4
[189] Rsolnp_1.16 gridExtra_2.3
[191] boot_1.3-28.1 igraph_1.6.0
[193] R6_2.5.1 labeling_0.4.3
[195] cluster_2.1.6 nloptr_2.0.3
[197] DelayedArray_0.28.0 tidyselect_1.2.0
[199] vipor_0.4.7 plotrix_3.8-4
[201] maps_3.4.2 xml2_1.3.6
[203] ggforce_0.4.1 fontBitstreamVera_0.1.1
[205] car_3.1-2 future_1.33.1
[207] rsvd_1.0.5 munsell_0.5.0
[209] KernSmooth_2.23-22 fontquiver_0.2.1
[211] htmlwidgets_1.6.4 ComplexHeatmap_2.16.0
[213] rlang_1.1.2 spatstat.sparse_3.0-3
[215] spatstat.explore_3.2-5 uuid_1.1-1
[217] colorRamps_2.3.1 HardyWeinberg_1.7.5
[219] ggnewscale_0.4.9 fansi_1.0.6
[221] beeswarm_0.4.0